Americans spend over 90% of their activity related energy expenditure performing common daily activities. Health care professionals use normative data as a guide to prescribe appropriate activities for patients and clients. Additionally, scientists use this resource to plan physical activity or nutritional interventions ad apply these estimates to epidemiological research. However, while normative data have existed for over 20 years and are seemingly accurate in young adults, they can lead to misguided estimates of the metabolic cost of daily activities in older adults. This is not a trivial issue sice physical activity is one of the only known modalities to improve physical function in older adults and plays a critical role in regulating body weight. However, there is a serious lack of information pertaining to potential age-related differences in the metabolic cost of daily activitis. This leaves a major gap in knowledge for properly prescribing physical activity for a population that has elevated risk cardiopulmonary and orthopedic impairments. The primary goal of this project is to test the hypothesis that aging is associated with a difference in the metabolic cost f doing exercise and lifestyle activities. We will assess pulmonary gas exchange in 210 adults aged 20 to 80+ years with a portable indirect calorimeter worn while performing 38 daily activities. We will examine the metabolic equivalent (MET as a function of 3.5 milliliter min-1kg-1), metabolic economy (energy expended for a given work rate) and relative metabolic cost (as a function of resting and peak oxygen consumption) for each task as a function of age. Secondly, we will address how metabolic costs of daily activities are affected by having functional impairments by testing an additional 90 older adults (60+ years) with functional impairment. Thirdly, because scientists and public health officials alike rely on perception-based exertion to monitor intensity of physical activity, we will address the question- Is aging associated with inaccuracies for self-gauging perceived exertion? Addressing this question will gain insight into a better delivery system for recommending physical intensity to older adults. Lastly, the design and comprehensive metabolic measurements being proposed will provide an unprecedented opportunity to validate accelerometers for estimating the type and intensity of physical activity. Using new mathematical techniques that apply machine learning approaches (random forests, support vector and multiple kernel learning techniques), we will assess the potential to reduce the error in estimating the type and intensity of physical activity as compared to traditional methods. There are many end products of this research. First, the work will produce the largest dataset of metabolic cost for daily activities in 60+ years old. Second, an age-correction factor for metabolic costs will be created to apply to hundreds of tasks that fall into similar categories as those being evaluated. Finally, the work will refine the tools needed to feasibly assess physical activity in young and old adults. These accomplishments will directly impact the fields of epidemiology, geriatric medicine, rehabilitation, and nutritional sciences.